
is the space of regression trees (CART [1]}), \(q\)  is the tree structure which maps a sample \(\mathbf {x}\)  to the corresponding leaf index in the tree with \(|L|\)  leaves, and \(w\)  is the leaf weight.
Each \(f_k\)  is an independent tree with its own structure \(q_k\)  and leaf weight \(w_{q_k}\) .
The \(K\)  functions (trees) will be learned by minimizing the regularized objective [2]}:
\(\small \mathcal {L}(\phi )&=\sum _{i=1}^n l(\hat{y}_i,y_i)+\sum _{k=1}^K\Omega (f_k),\) 
